O curso será realizado entre os dias 4 e 15 de março de 2024, segunda a sexta, de 9h as 16h, no Campus Manguinhos-Maré da Fiocruz, Rio de Janeiro. Terá como público alvo alunos de pós-graduação e demais profissionais de pesquisa, dos mais diversos níveis de formação, que estejam realizando ou planejando realizar experimentos de single-cell transcriptomics.
A ideia é contribuir para a formação desses pesquisadores, através de conceitos e dicas práticas, desde o desenho experimental até a análise dos dados, além de propiciar trocas de experiências e possibilidades de colaborações.
As inscrições estão abertas, pelo campus virtual, link: https://campusvirtual.fiocruz.br/gestordecursos/hotsite/diggingdeep/apresentaaao/8868, até o dia 9 de fevereiro. Os participantes serão selecionados conforme carta de motivação.
O curso, que será ministrado em inglês, faz parte do Programa Capes-PrInt da Fiocruz e conta com apoio das Pós-Graduações em Biologia Celular e Molecular (BCM) e Biologia Computacional e Sistemas (BCS) do IOC/Fiocruz. Alunos de pós-graduação poderão validar este curso internacional como 2 créditos.
Digging deep into chronic infection through single-cell transcriptomics
This international course will take place at Fiocruz, Rio de Janeiro, Campus Manguinhos-Maré, between March 4th and 15th, 2024. There will be theoretical and practical classes from 9 am to 4 pm.
Students will be selected according to their motivation letter (please, fill in the form at https://campusvirtual.fiocruz.br/gestordecursos/hotsite/diggingdeep/apresentaaao/8868 before February, 9th).
Learning Objectives
Upon successful completion of this course, students will be able to:
1. Understand the importance of single-cell transcriptomics vs bulk transcriptomics
2. What are the different methods of doing single-cell transcriptomics
3. How transcriptomics is different from “RNA sequencing”
4. Use basic command line tools
5. Importance of pre-processing of transcriptomics data
6. Access the quality of your data
7. Batch effects and how they affect your data
8. Data cleaning and normalization
9. Differentiate between data analysis pipelines. What to use for your studies?
10. Creating a reference genome (if your samples are not human samples)
11. Hypothesis-driven vs open-ended data analysis
12. Map reads to a genome, generate downstream metrics, and evaluate mapping quality.
13. Delineating “mechanistic” data out of single-cell transcriptomics
14. Illustrate how commonly used bioinformatics tools and techniques can produce incorrect or misleading results if not applied correctly. Illustrate common pitfalls of commonly-used bioinformatics tools.
15. Devise appropriate positive and negative controls for use in computational experiments.
16. Implement a reproducible computational workflow.
17. How to interpret your data and apply the output to real-world problems
18. Data reproducibility, rigor, and management. Applying FAIR principles to your data
* Students must bring their own laptops for practical classes
Course Organizers: Dr. Leticia Lery, Dr. Thiago Parente (IOC/Fiocruz)
Course instructors: Dr. Taru S. Dutt (Colorado State University); Dr. Thiago Parente and the Fiocruz bioinformatics platform team